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Human Vaccines & Immunotherapeutics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/khvi20
Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection ab
c
cd
a
a
Christoph T. Berger , Victor Greiff , Matthias Mehling , Stefanie Fritz , Marc A. Meier , a
e
ab
f
c
Gideon Hoenger , Anna Conen , Mike Recher , Manuel Battegay , Sai T. Reddy & Christoph ab
Hess a
Department of Biomedicine, University Hospital Basel, Basel, Switzerland
b
Medical Outpatient Clinic, Department of Internal Medicine, University Hospital Basel, Basel, Switzerland
Click for updates
c
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
d
Neurology Clinic, University Hospital Basel, Basel, Switzerland
e
Infectious Disease Unit, Kantonsspital Aarau, Switzerland
f
Infectious Disease Clinic, University Hospital Basel, Basel, Switzerland Accepted author version posted online: 18 Feb 2015.
To cite this article: Christoph T. Berger, Victor Greiff, Matthias Mehling, Stefanie Fritz, Marc A. Meier, Gideon Hoenger, Anna Conen, Mike Recher, Manuel Battegay, Sai T. Reddy & Christoph Hess (2015): Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection, Human Vaccines & Immunotherapeutics To link to this article: http://dx.doi.org/10.1080/21645515.2015.1008930
Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also.
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection Christoph T. Berger1,2,#, Victor Greiff3,#, Matthias Mehling3,4, Stefanie Fritz1, Marc A. Meier1, Gideon Hoenger1, Anna Conen5, Mike Recher1,2, Manuel Battegay6, Sai T. Reddy3, #, Christoph
ip t
Department of Biomedicine, University Hospital Basel, Basel, Switzerland
2
Medical Outpatient Clinic, Department of Internal Medicine, University Hospital Basel, Basel,
cr
1
Switzerland
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
4
Neurology Clinic, University Hospital Basel, Basel, Switzerland
5
Infectious Disease Unit, Kantonsspital Aarau, Switzerland
6
Infectious Disease Clinic, University Hospital Basel, Basel, Switzerland authors contributed equally
Corresponding Author: Christoph T. Berger, MD
M
# These
an
us
3
ed
Medical Outpatient Clinic and Translational Immunology Lab Departments of Internal Medicine and Biomedicine University Hospital Basel
pt
Petersgraben 4, 4054 Basel, Switzerland
ce
Email: [email protected]
The authors declare no conflict of interest. The work was supported by the Swiss National
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Hess1,2 #
Science Foundation (SNSF) to CTB [PZ00P3-148000] and CH [SNSF 310030_153059], and supported by the Misrock Foundation and SystemsX.ch Antibody Research Technology and Development (RTD) to STR.
1
Abstract Background: Vaccines dramatically reduce infection-related morbidity and mortality. Determining factors that modulate the host response is key to rational vaccine design and demands unsupervised analysis. Methods: To longitudinally resolve influenza-specific humoral immune response dynamics we
ip t
constructed vaccine response profiles of influenza A- and B-specific IgM and IgG levels from 42 healthy and 31 HIV infected influenza-vaccinated individuals. Pre-vaccination antibody levels and levels at three predefined time points after vaccination were included in each profile. We
cr
associated immune dysfunction, adaptive immune factors (pre-existing influenza-specific
us
antibodies, T cell responses), an innate immune factor (Mannose Binding Lectin, MBL),
demographic characteristics (gender, age), or the vaccine preparation (split vs. virosomal)
an
impacted the immune response to influenza vaccination.
Results: Hierarchical clustering associated vaccine preparation and pre-existing IgG levels with the profiles of healthy individuals. In contrast to previous in vitro and animal data, MBL
M
levels had no impact on the adaptive vaccine response. Importantly, while HIV infected subjects with low CD4 T cell counts showed a reduced magnitude of their vaccine response,
ed
their response profiles were indistinguishable from those of healthy controls, suggesting quantitative but not qualitative deficits.
Conclusion: Unsupervised profile-based analysis ranks factors impacting the vaccine-response
pt
by relative importance, with substantial implications for comparing, designing and improving vaccine preparations and strategies. Profile similarity between HIV infected and HIV negative
ce
individuals suggests merely quantitative differences in the vaccine response in these individuals, offering a rationale for boosting strategies in the HIV infected population.
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
performed hierarchical clustering on these profiles to study the extent to which HIV infection
Key words: HIV, immune dysfunction, influenza vaccination, unsupervised analysis, immune response, MBL, vaccine preparation
2
Each year, influenza virus infection affects 5 to 20% of the world’s population 1. Annual influenza vaccination greatly contributes to reduce influenza-related morbidity and mortality 2. However, vaccine efficacy varies between 40-90%, depending on the patient group, the vaccine preparation, and the match of vaccine and circulating epidemic virus strain 1, 3, 4. Especially
ip t
immunocompromised populations, including HIV infected individuals, often fail to mount protective vaccine responses 5, 6. Therefore, understanding how immunological and non-
cr
enhanced vaccines and improved vaccination schemes.
us
Pathogen-specific peak antibody levels are routinely used as surrogates of vaccine efficacy.
an
However, a protective immune response relies on robust immune memory formation, which may not be fully captured by peak antibody titers alone, but rather by time-resolved
M
longitudinal titer measurements, which would reflect more closely the dynamics and memory of long-term immunological protection in vivo. Moreover, conventional comparison of cross-
ed
sectional antibody peak titers is affected by confounding factors such as the sampling timepoint and presence of pre-existing immunity (antigen-specific memory B cells), and thus may
pt
not accurately reflect the true immunological properties of vaccine responses 7. Recent efforts have, therefore, shifted to characterization of the vaccine response based on longitudinal data,
ce
coupled with unsupervised analyses –in order to unambiguously identify factors that impact vaccine response-dynamics 8-11. In this study, we used time-resolved measurements of the influenza-specific antibody
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
immunological factors impact the vaccine response in humans is crucial both for the design of
response to generate immunological profiles –hereafter termed ‘vaccine response profiles’ – across a cohort of HIV negative and HIV infected individuals following influenza vaccination. These profiles allowed an unsupervised analysis of the association of individual vaccine responses with various factors, namely immune dysfunction associated with HIV infection, adaptive immune factors (pre-existing influenza-specific antibodies, T cell responses), an
3
innate immune factor (Mannose Binding Lectin, MBL), demographic characteristics (gender, age), or the vaccine preparation (split vs. virosomal). The effect of MBL, a pattern recognition molecule involved in the containment of infections, was investigated since it has been previously shown to have anti-influenza effects in vitro 12 and has been suggested to affect
ip t
murine vaccine responses in vivo 13. The impact of MBL on the adaptive immune response to influenza vaccination in humans has, to our knowledge, not been investigated yet.
cr
of the various factors on the evolution of the humoral vaccine-response, which may help guide
an
us
future efforts to improve vaccines.
Influenza vaccine responses from previously published local vaccination cohorts were
M
analyzed 6, 14. Study participants were recruited at the University Hospital Basel, Switzerland, and vaccinated with either a trivalent virosomal vaccine (Inflexal V®, Berna Biotech) (season
ed
2007/2008, n=24 healthy and 31 HIV infected individuals), or an inactivated influenza virus split-vaccine (Mutagrip®, Sanofi Pasteur) (season 2008/2009, n=18) 6, 14. The study was
pt
approved by the local ethics committee and all participants gave informed consent. Cohort characteristics, vaccine composition and type are summarized in Table 1. Influenza-specific
ce
IgM and IgG serum levels were available immediately prior to vaccination, and at days 7, 14 and 21 post-vaccination (Supplementary Fig. S1). These have been quantified using commercially available ELISA Kits (Genzyme Virotech), as previously described 6, 14. Antibody
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Using an unsupervised analysis of vaccine response profiles allowed us to rank the impact
levels ≥10 Virotech Units (VE)/mL were considered protective. Influenza-specific T cells were measured using standard interferon-γ (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assays15. Influenza antigen (Inflexal V®, Berna Biotech) was added at a final concentration of 14 μg/mL to 200.000 peripheral blood mononuclear cells (PBMC) and incubated for 16 hours. Plates were developed using an alkaline phosphatase coupled detection antibody (7-B6-1-ALP,
4
Mabtech) and the HistoMark RED phosphatase system (KPL, Gaithersburg, Maryland, USA). Spots were counted with the ELISpot Reader System (CSR01, AID GmbH, Strassberg, Germany). Results were expressed as spot-forming cells/million PBMC (SFC/M PBMC) with a cutoff for a positive response of 50 SFC/M PBMC. All measurements were performed in duplicates and
ip t
PBMC stimulated with Phytohemagglutinin (PHA) (1.8 μg/mL; REMEL, Oxoid AG, Basel, Switzerland) served as a positive control. For this study, MBL levels, which tightly correlate
cr
commercially available MBL Oligomer ELISA Kit according to the manufacturers instructions
us
(KIT 029, BioPorto, Denmark).
an
Against this background, we sought to investigate the extent to which predefined demographic, adaptive and innate immune factors influence the influenza-specific vaccine
M
response in healthy individuals. In order to recapitulate vaccine-specific immunological characteristics on the individual level in a time-resolved fashion, we built vaccine response
ed
profiles consisting of each individual’s anti-influenza A and B IgM or IgG levels measured at the four indicated time points (Supplementary Fig. S1) in order to capture the humoral immune
pt
response at a higher dimensionality compared to traditional baseline-peak comparisons 16. These profiles were tested for similarity and clustered accordingly, without prior knowledge
ce
(i.e., unsupervised) of information on a vaccinated individual’s relevant characteristics 16-18. Hierarchical clustering of vaccine response profiles was performed using the “average” clustering algorithm from the R function hclust. Employing Pearson correlation as distance
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
with MBL function, were assessed in serum of the healthy vaccinated individuals using the
metric allowed us to cluster profiles independently of baseline IgM or IgG levels, which enabled us to compare the dynamics of the humoral immune response in vivo regardless of individual variances in baseline IgM and IgG levels – as opposed to traditional baseline-peak comparisons. The significance of clusters was assessed using the pvclust R package 19. The association of vaccine response profile clustering with the following factors was assessed in healthy subjects:
5
vaccine preparation (trivalent virosomal vs. inactivated split), demographic (age, gender), adaptive immunity (pre-existing influenza-specific IgG levels, influenza-specific T cell response), and innate immunity (circulating levels of Mannose Binding Lectin (MBL)).
ip t
As previously published 6, 14, vaccination of healthy individuals induced a significant influenza-specific cellular and humoral immune response, peaking at day 14 post-vaccination
cr
antibody levels, 17/42 healthy individuals (40%) had pre-existing IgG against influenza A and
us
25/42 (60%) against influenza B. In contrast, only 2/42 (5%) had elevated IgM levels against
an
influenza A or B. While both vaccine regimens were found to generate robust cellular and humoral immune responses, IgM responses, in contrast to IgG, were more pronounced for the
M
2008/2009 cohort (Supplementary Fig. S1). MBL levels ranged from 17 to 6900 ng/mL, including six individuals with MBL deficiency (MBL level 350/μl
CD4≤350/μl
(2008/2009)
24
22
9
18
40.8 (22–76)
46 (21–52)
46 (30–65)
% Female
45.8%
36.4%
Median CD4
n.d.
545 (364–941)
n.d.
median), pre-existing IgG levels (green: ≤ median, yellow: >median), pre-existing T cells (dark grey: ≤ median, light grey: >median) and gender (black: female, yellow: male). Influenza A profiles clustered dependent
15
on the pre-existing IgG against influenza A (G, green: ≤ median, yellow: >median), which was confirmed by a strong inverse correlation between these markers (H). The same was true for pre-existing IgG against influenza B (I, J). There was no clustering based on MBL levels (darkgreen: ≤ median, purple: >median) (G, H). Spearman Ranks correlation analysis was
ip t
performed in Figures H and J. The clustering by pre-existing IgG was determined to be
cr us an M ed pt ce Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
significant (pmedian) as well as low
cr us an M ed pt ce Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
ip t
(closed squares: ≤median) and high (crosses: >median) pre-existing IgG levels are displayed.
18
19
pt
ce
Ac ed ip t
cr
us
an
M
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Figure 3: Immunoglobulin profiles do not cluster by HIV status or level of immunosuppression. (A) Peak IgM and IgG responses against influenza A and B are shown as VE/mL. HD = healthy donor, CD4 high = HIV infected individuals with CD4 counts >350/μl, CD4 low = HIV infected individuals with CD4 counts ≤350/μl. *p
Human Vaccines & Immunotherapeutics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/khvi20
Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection ab
c
cd
a
a
Christoph T. Berger , Victor Greiff , Matthias Mehling , Stefanie Fritz , Marc A. Meier , a
e
ab
f
c
Gideon Hoenger , Anna Conen , Mike Recher , Manuel Battegay , Sai T. Reddy & Christoph ab
Hess a
Department of Biomedicine, University Hospital Basel, Basel, Switzerland
b
Medical Outpatient Clinic, Department of Internal Medicine, University Hospital Basel, Basel, Switzerland
Click for updates
c
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
d
Neurology Clinic, University Hospital Basel, Basel, Switzerland
e
Infectious Disease Unit, Kantonsspital Aarau, Switzerland
f
Infectious Disease Clinic, University Hospital Basel, Basel, Switzerland Accepted author version posted online: 18 Feb 2015.
To cite this article: Christoph T. Berger, Victor Greiff, Matthias Mehling, Stefanie Fritz, Marc A. Meier, Gideon Hoenger, Anna Conen, Mike Recher, Manuel Battegay, Sai T. Reddy & Christoph Hess (2015): Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection, Human Vaccines & Immunotherapeutics To link to this article: http://dx.doi.org/10.1080/21645515.2015.1008930
Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also.
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection Christoph T. Berger1,2,#, Victor Greiff3,#, Matthias Mehling3,4, Stefanie Fritz1, Marc A. Meier1, Gideon Hoenger1, Anna Conen5, Mike Recher1,2, Manuel Battegay6, Sai T. Reddy3, #, Christoph
ip t
Department of Biomedicine, University Hospital Basel, Basel, Switzerland
2
Medical Outpatient Clinic, Department of Internal Medicine, University Hospital Basel, Basel,
cr
1
Switzerland
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
4
Neurology Clinic, University Hospital Basel, Basel, Switzerland
5
Infectious Disease Unit, Kantonsspital Aarau, Switzerland
6
Infectious Disease Clinic, University Hospital Basel, Basel, Switzerland authors contributed equally
Corresponding Author: Christoph T. Berger, MD
M
# These
an
us
3
ed
Medical Outpatient Clinic and Translational Immunology Lab Departments of Internal Medicine and Biomedicine University Hospital Basel
pt
Petersgraben 4, 4054 Basel, Switzerland
ce
Email: [email protected]
The authors declare no conflict of interest. The work was supported by the Swiss National
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Hess1,2 #
Science Foundation (SNSF) to CTB [PZ00P3-148000] and CH [SNSF 310030_153059], and supported by the Misrock Foundation and SystemsX.ch Antibody Research Technology and Development (RTD) to STR.
1
Abstract Background: Vaccines dramatically reduce infection-related morbidity and mortality. Determining factors that modulate the host response is key to rational vaccine design and demands unsupervised analysis. Methods: To longitudinally resolve influenza-specific humoral immune response dynamics we
ip t
constructed vaccine response profiles of influenza A- and B-specific IgM and IgG levels from 42 healthy and 31 HIV infected influenza-vaccinated individuals. Pre-vaccination antibody levels and levels at three predefined time points after vaccination were included in each profile. We
cr
associated immune dysfunction, adaptive immune factors (pre-existing influenza-specific
us
antibodies, T cell responses), an innate immune factor (Mannose Binding Lectin, MBL),
demographic characteristics (gender, age), or the vaccine preparation (split vs. virosomal)
an
impacted the immune response to influenza vaccination.
Results: Hierarchical clustering associated vaccine preparation and pre-existing IgG levels with the profiles of healthy individuals. In contrast to previous in vitro and animal data, MBL
M
levels had no impact on the adaptive vaccine response. Importantly, while HIV infected subjects with low CD4 T cell counts showed a reduced magnitude of their vaccine response,
ed
their response profiles were indistinguishable from those of healthy controls, suggesting quantitative but not qualitative deficits.
Conclusion: Unsupervised profile-based analysis ranks factors impacting the vaccine-response
pt
by relative importance, with substantial implications for comparing, designing and improving vaccine preparations and strategies. Profile similarity between HIV infected and HIV negative
ce
individuals suggests merely quantitative differences in the vaccine response in these individuals, offering a rationale for boosting strategies in the HIV infected population.
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
performed hierarchical clustering on these profiles to study the extent to which HIV infection
Key words: HIV, immune dysfunction, influenza vaccination, unsupervised analysis, immune response, MBL, vaccine preparation
2
Each year, influenza virus infection affects 5 to 20% of the world’s population 1. Annual influenza vaccination greatly contributes to reduce influenza-related morbidity and mortality 2. However, vaccine efficacy varies between 40-90%, depending on the patient group, the vaccine preparation, and the match of vaccine and circulating epidemic virus strain 1, 3, 4. Especially
ip t
immunocompromised populations, including HIV infected individuals, often fail to mount protective vaccine responses 5, 6. Therefore, understanding how immunological and non-
cr
enhanced vaccines and improved vaccination schemes.
us
Pathogen-specific peak antibody levels are routinely used as surrogates of vaccine efficacy.
an
However, a protective immune response relies on robust immune memory formation, which may not be fully captured by peak antibody titers alone, but rather by time-resolved
M
longitudinal titer measurements, which would reflect more closely the dynamics and memory of long-term immunological protection in vivo. Moreover, conventional comparison of cross-
ed
sectional antibody peak titers is affected by confounding factors such as the sampling timepoint and presence of pre-existing immunity (antigen-specific memory B cells), and thus may
pt
not accurately reflect the true immunological properties of vaccine responses 7. Recent efforts have, therefore, shifted to characterization of the vaccine response based on longitudinal data,
ce
coupled with unsupervised analyses –in order to unambiguously identify factors that impact vaccine response-dynamics 8-11. In this study, we used time-resolved measurements of the influenza-specific antibody
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
immunological factors impact the vaccine response in humans is crucial both for the design of
response to generate immunological profiles –hereafter termed ‘vaccine response profiles’ – across a cohort of HIV negative and HIV infected individuals following influenza vaccination. These profiles allowed an unsupervised analysis of the association of individual vaccine responses with various factors, namely immune dysfunction associated with HIV infection, adaptive immune factors (pre-existing influenza-specific antibodies, T cell responses), an
3
innate immune factor (Mannose Binding Lectin, MBL), demographic characteristics (gender, age), or the vaccine preparation (split vs. virosomal). The effect of MBL, a pattern recognition molecule involved in the containment of infections, was investigated since it has been previously shown to have anti-influenza effects in vitro 12 and has been suggested to affect
ip t
murine vaccine responses in vivo 13. The impact of MBL on the adaptive immune response to influenza vaccination in humans has, to our knowledge, not been investigated yet.
cr
of the various factors on the evolution of the humoral vaccine-response, which may help guide
an
us
future efforts to improve vaccines.
Influenza vaccine responses from previously published local vaccination cohorts were
M
analyzed 6, 14. Study participants were recruited at the University Hospital Basel, Switzerland, and vaccinated with either a trivalent virosomal vaccine (Inflexal V®, Berna Biotech) (season
ed
2007/2008, n=24 healthy and 31 HIV infected individuals), or an inactivated influenza virus split-vaccine (Mutagrip®, Sanofi Pasteur) (season 2008/2009, n=18) 6, 14. The study was
pt
approved by the local ethics committee and all participants gave informed consent. Cohort characteristics, vaccine composition and type are summarized in Table 1. Influenza-specific
ce
IgM and IgG serum levels were available immediately prior to vaccination, and at days 7, 14 and 21 post-vaccination (Supplementary Fig. S1). These have been quantified using commercially available ELISA Kits (Genzyme Virotech), as previously described 6, 14. Antibody
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Using an unsupervised analysis of vaccine response profiles allowed us to rank the impact
levels ≥10 Virotech Units (VE)/mL were considered protective. Influenza-specific T cells were measured using standard interferon-γ (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assays15. Influenza antigen (Inflexal V®, Berna Biotech) was added at a final concentration of 14 μg/mL to 200.000 peripheral blood mononuclear cells (PBMC) and incubated for 16 hours. Plates were developed using an alkaline phosphatase coupled detection antibody (7-B6-1-ALP,
4
Mabtech) and the HistoMark RED phosphatase system (KPL, Gaithersburg, Maryland, USA). Spots were counted with the ELISpot Reader System (CSR01, AID GmbH, Strassberg, Germany). Results were expressed as spot-forming cells/million PBMC (SFC/M PBMC) with a cutoff for a positive response of 50 SFC/M PBMC. All measurements were performed in duplicates and
ip t
PBMC stimulated with Phytohemagglutinin (PHA) (1.8 μg/mL; REMEL, Oxoid AG, Basel, Switzerland) served as a positive control. For this study, MBL levels, which tightly correlate
cr
commercially available MBL Oligomer ELISA Kit according to the manufacturers instructions
us
(KIT 029, BioPorto, Denmark).
an
Against this background, we sought to investigate the extent to which predefined demographic, adaptive and innate immune factors influence the influenza-specific vaccine
M
response in healthy individuals. In order to recapitulate vaccine-specific immunological characteristics on the individual level in a time-resolved fashion, we built vaccine response
ed
profiles consisting of each individual’s anti-influenza A and B IgM or IgG levels measured at the four indicated time points (Supplementary Fig. S1) in order to capture the humoral immune
pt
response at a higher dimensionality compared to traditional baseline-peak comparisons 16. These profiles were tested for similarity and clustered accordingly, without prior knowledge
ce
(i.e., unsupervised) of information on a vaccinated individual’s relevant characteristics 16-18. Hierarchical clustering of vaccine response profiles was performed using the “average” clustering algorithm from the R function hclust. Employing Pearson correlation as distance
Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
with MBL function, were assessed in serum of the healthy vaccinated individuals using the
metric allowed us to cluster profiles independently of baseline IgM or IgG levels, which enabled us to compare the dynamics of the humoral immune response in vivo regardless of individual variances in baseline IgM and IgG levels – as opposed to traditional baseline-peak comparisons. The significance of clusters was assessed using the pvclust R package 19. The association of vaccine response profile clustering with the following factors was assessed in healthy subjects:
5
vaccine preparation (trivalent virosomal vs. inactivated split), demographic (age, gender), adaptive immunity (pre-existing influenza-specific IgG levels, influenza-specific T cell response), and innate immunity (circulating levels of Mannose Binding Lectin (MBL)).
ip t
As previously published 6, 14, vaccination of healthy individuals induced a significant influenza-specific cellular and humoral immune response, peaking at day 14 post-vaccination
cr
antibody levels, 17/42 healthy individuals (40%) had pre-existing IgG against influenza A and
us
25/42 (60%) against influenza B. In contrast, only 2/42 (5%) had elevated IgM levels against
an
influenza A or B. While both vaccine regimens were found to generate robust cellular and humoral immune responses, IgM responses, in contrast to IgG, were more pronounced for the
M
2008/2009 cohort (Supplementary Fig. S1). MBL levels ranged from 17 to 6900 ng/mL, including six individuals with MBL deficiency (MBL level 350/μl
CD4≤350/μl
(2008/2009)
24
22
9
18
40.8 (22–76)
46 (21–52)
46 (30–65)
% Female
45.8%
36.4%
Median CD4
n.d.
545 (364–941)
n.d.
median), pre-existing IgG levels (green: ≤ median, yellow: >median), pre-existing T cells (dark grey: ≤ median, light grey: >median) and gender (black: female, yellow: male). Influenza A profiles clustered dependent
15
on the pre-existing IgG against influenza A (G, green: ≤ median, yellow: >median), which was confirmed by a strong inverse correlation between these markers (H). The same was true for pre-existing IgG against influenza B (I, J). There was no clustering based on MBL levels (darkgreen: ≤ median, purple: >median) (G, H). Spearman Ranks correlation analysis was
ip t
performed in Figures H and J. The clustering by pre-existing IgG was determined to be
cr us an M ed pt ce Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
significant (pmedian) as well as low
cr us an M ed pt ce Ac
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
ip t
(closed squares: ≤median) and high (crosses: >median) pre-existing IgG levels are displayed.
18
19
pt
ce
Ac ed ip t
cr
us
an
M
Downloaded by [The Aga Khan University] at 19:56 01 March 2015
Figure 3: Immunoglobulin profiles do not cluster by HIV status or level of immunosuppression. (A) Peak IgM and IgG responses against influenza A and B are shown as VE/mL. HD = healthy donor, CD4 high = HIV infected individuals with CD4 counts >350/μl, CD4 low = HIV infected individuals with CD4 counts ≤350/μl. *p
